Posts filed under Sports statistics (1776)

Team Ratings for Round 7

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 36 matches played, 24 of which were correctly predicted, a success rate of 66.7%.

Here are the predictions for last week’s games.

Predictions for Round 7

Here are the predictions for Round 7. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

via Alberto Cairo, this gem from Malaprensa, a Spanish mediawatch site, originally from Marca.

This isn’t actually a pie chart, it’s a bar chart that has been horribly warped around a circle.  It shows top transfer fees in football (ie, soccer). One Neymar da Silva Santos Júnior has allegedly ended up with a transfer fee estimated at 111 million euros, through complicated arrangements. This would be a record; the originally announced figure was a mere 57 million euros, which would put Neymar in tenth place alongside Hernan Crespo

Malaprensa points out that the figures aren’t inflation-adjusted, and that they aren’t including comparable sets of payments for all the players. They don’t point out how bad the display is: compare the heights for 57 and 111 million euro, and then think about what the area comparison would be.

I’ve redrawn the bars in a sensible coordinate system,  showing the apparent differences based on the height, area, nominal euro amount, and euro amount adjusted for inflation (the last is from Malaprensa), with Crespo’s transfer fee scaled to 1 in each case

It’s much less impressive when it’s shown accurately.

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

This week I don’t have full details because I have limited internet access and am having to copy the details from my computer.
 

Predictions for Round 2

Here are the predictions for Round 2. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for Round 5

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 22 matches played, 14 of which were correctly predicted, a success rate of 63.6%.

Here are the predictions for last week’s games.

Predictions for Round 5

Here are the predictions for Round 5. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for Round 4

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

My predictions are early this week and may not appear next week unless I can get some internet access while travelling.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 16 matches played, 8 of which were correctly predicted, a success rate of 50%.

Here are the predictions for last week’s games.

Predictions for Round 4

Here are the predictions for Round 4. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for Round 1

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the seaso

Predictions for Round 1

Here are the predictions for Round 1. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for Round 3

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 9 matches played, 3 of which were correctly predicted, a success rate of 33.3%.

Here are the predictions for last week’s games.

Predictions for Round 3

Here are the predictions for Round 3. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for Round 1

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons. Still the Crusaders have a high rating. Despite being tempted to arbitrarily change their rating, I have stuck with the formula. You may wish to downgrade them. The replacement of the Kings by the Lions provides another problem. In the past I have given a rating of -10 to an unknown team entering the competition, and this has seemed to provide reasonable predictions. The Lions do have a rating from when they were previously in the competition so I have used that rating. Again, you may wish to lower their rating.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Predictions for Round 1

Here are the predictions for Round 1. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

It was with some amazement that I read the following in the NZ Herald:

Since his first test in charge at Cape Town 13 months ago, McCullum has won just five out of 13 test tosses. Add in losing all five ODIs against India and it does not make for particularly pretty reading.

Then again, he’s up against another ordinary tosser in MS Dhoni, who has got it right just 21 times out of 51 tests at the helm. Three of those were in India’s past three tests.

The implication of the author seems to be that five out of 13, or 21 out of 51 are rather unlucky for a set of random coin tosses, and that the possibility exists that they can influence the toss. They are unlucky if one hopes to win the coin toss more than lose it, but there is no reason to think that is a realistic expectation unless the captains know something about the coin that we don’t.

Again, simple application of the binomial distribution shows how ordinary these results are. If we assume that the chance of winning the toss is 50% (Pr(Win) = 0.5) each time, then in 13 throws we would expect to win, on average, 6 to 7 times (6.5 for the pedants). Random variation would mean that about 90% of the time, we would expect to see four to nine wins in 13 throws (on average). So McCullum’s five from 13 hardly seems unlucky, or exceptionally bad. You might be tempted to think that the same may not hold for Dhoni. Just using the observed data, his estimated probability of success is 21/51 or 0.412 (3dp). This is not 0.5, but again, assuming a fair coin, and independence between tosses, it is not that unreasonable either. Using frequentist theory, and a simple normal approximation (with no small sample corrections), we would expect 96.4% of sets of 51 throws to yield somewhere between 18 and 33 successes. So Dhoni’s results are somewhat on the low side, but they are not beyond the realms of reasonably possibility.

Taking a Bayesian stance, as is my wont, yields a similar result. If I assume a uniform prior – which says “any probability of success between 0 and 1 is equally likely”, and binomial sampling, then the posterior distribution for the probability of success follows a Beta distribution with parameters a = 21+ 1 = 22, and b = 51 – 21 + 1 = 31. There are a variety of different ways we might use this result. One is to construct a credible interval for the true value of the probability of success. Using our data, we can say there is about a 95% chance that the true value is between 0.29 and 0.55 – so again, as 0.5 is contained within this interval, it is possible. Alternatively, the posterior probability that the true probability of success is less than 0.5 is about 0.894 (3dp). That is high, but not high enough for me. It says there at about a 1 in 10 chance that the true probability of success could actually be 0.5 or higher.

Team Ratings for the ITM Cup Finals

Here are the team ratings prior to the ITM Cup Finals, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department home page to see this.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

There is a problem with the code I have been using for assessing performance, due to the unusual schedule in the ITM Cup, where some teams play more than one game in a week. I haven’t had time to alter the code so am omitting this section for the time being. Look at last week’s post to see how the predictions went. For the record, there were 3 games correct, out of 4 games played last week

Predictions for the ITM Cup Finals

Here are the predictions for the ITM Cup Finals. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.